Tested with more advanced methods of card detection

tools/feature_extraction.py

@@ -0,0 +1,99 @@
+import shenzhen_solitaire.card_detection.configuration as configuration
+from shenzhen_solitaire.board import NumberCard, SpecialCard
+
+import cv2
+
+import numpy as np
+from typing import Any, Tuple
+
+
+def prepare_image(image):
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    # edge_image = cv2.Canny(gray_image, 120, 160)
+    ret, edge_image = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY_INV)
+    contours2, hierarchy = cv2.findContours(
+        edge_image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE
+    )
+    cnt2 = max(contours2, key=cv2.contourArea)
+
+    mask = np.zeros(edge_image.shape, dtype=edge_image.dtype)
+    contim = cv2.drawContours(mask, [cnt2], 0, 1, cv2.FILLED)
+    crop = np.multiply(edge_image, contim)
+    return crop
+
+
+def match_template(image, template):
+    image_cont, hierarchy = cv2.findContours(
+        image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE
+    )
+    imcont = max(image_cont, key=cv2.contourArea)
+    template_cont, hierarchy = cv2.findContours(
+        template, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE
+    )
+    temcont = max(template_cont, key=cv2.contourArea)
+    return [
+        cv2.matchShapes(imcont, temcont, mode, 0.0)
+        for mode in (
+            cv2.CONTOURS_MATCH_I1,
+            cv2.CONTOURS_MATCH_I2,
+            cv2.CONTOURS_MATCH_I3,
+        )
+    ]
+
+
+def type_fine(one, other) -> bool:
+    if isinstance(one, SpecialCard):
+        return one == other
+    assert isinstance(one, NumberCard)
+    if not isinstance(other, NumberCard):
+        return False
+    return one.number == other.number
+
+
+def debug_match(image, image_type, catalogue):
+    img1 = prepare_image(image)
+    i1_matches = []
+    for index, (template_image, template_type) in enumerate(catalogue):
+        img2 = prepare_image(template_image)
+        i1_matches.append((template_type, match_template(img1, img2)[0], index))
+    i1_matches = sorted(i1_matches, key=lambda x: x[1])
+    if not type_fine(i1_matches[0][0], image_type):
+        correct_index = 0
+        for list_type, list_value, _ in i1_matches:
+            if type_fine(list_type, image_type):
+                correct_value = list_value
+                break
+            correct_index += 1
+        print(
+            f"{str(image_type):>20} matched as {str(i1_matches[0][0]):>20} {i1_matches[0][1]:.05f}, "
+            f"correct in pos {correct_index:02d} val {correct_value:.05f}"
+        )
+        cv2.imshow("one", prepare_image(catalogue[i1_matches[0][2]][0]))
+        cv2.imshow("two", img1)
+        cv2.imshow("three", prepare_image(catalogue[i1_matches[correct_index][2]][0]))
+        cv2.waitKey(0)
+        return
+    for list_type, list_value, list_index in i1_matches:
+        if not type_fine(list_type, i1_matches[0][0]):
+            if list_value * 0.6 < i1_matches[0][1]:
+                print(
+                    f"{str(image_type):>20} {i1_matches[0][1]:.05f} very close"
+                    f" match with {str(list_type):>20} {list_value:.05f}"
+                )
+            return
+
+    if i1_matches[0][1] > 1:
+        print(f"{image_type} with value {i1_matches[0][1]}")
+
+
+def main() -> None:
+    pc = configuration.load("test_config.zip")
+    laptop = configuration.load("laptop_conf.zip")
+    bla = [(i, t) for i, t in pc.catalogue if t == SpecialCard.Hua]
+    bla = pc.catalogue
+    for pc_image, pc_card_type in bla:
+        debug_match(pc_image, pc_card_type, laptop.catalogue)
+
+
+if __name__ == "__main__":
+    main()